The present disclosure relates to a device analytics, and more particularly, to providing telemetry-based analytics to identify and remediate top end-user impacting issues on computing devices of users in an information technology network.
Devices that run software may usually require updates over time. The need for software updates may be driven by many factors, such as addressing bugs, adding new functionality, improving performance, maintaining compatibility with other software, and so forth. While many techniques have been used for updating software, an update typically involves changing the source code of a program, compiling the program, and distributing the program to devices where the updated program will be executed.
The increasing network connectivity of devices has led to higher rates of updating by software developers and more frequent reporting of performance-related data (telemetry) by devices. In a short time period, a device may receive many software updates and may transmit many telemetry reports to a variety of telemetry collectors. A software distribution system may rapidly issue many different software updates to many different devices. As devices provide feedback telemetry about performance, crashes, stack dumps, execution traces, etc., around the same time, many software components on the devices might be changing. Therefore, it can be difficult for an information technology administrator and/or software developer to use the telemetry feedback to decide whether a particular software update created or fixed any problems. If an anomaly is occurring on some devices, it can be difficult to determine whether any particular software update is implicated, any conditions under which an update might be linked to an anomaly, or what particular code-level changes in a software update are implicated. In short, high rates of software updating and telemetry reporting, perhaps by devices with varying architectures and operating systems, has made it difficult to find correlations between software updates (or source code changes) and error events and anomalies manifested in telemetry feedback.
Thus, there is a need in the art for improvements in locating error events and/or anomalies in telemetry data and finding correlations between the error events and/or anomalies and software updates on computer devices.